Automatic distortion-suppressed time series fitting method for irregular sampled NDVI

Abstract. Global climate change has significantly affected vegetation variation in the third-polar region of the world – the Qinghai-Tibet Plateau. As one of the most important indicators of vegetation variation (growth, coverage and tempo-spatial change), the Normalized Difference Vegetation Index (NDVI) is widely employed to study the response of vegetation to climate change. However, a long-term series analysis cannot be achieved because a single data source is constrained by time sequence. Therefore, a new framework was presented in this paper to extend the product series of monthly NDVI, taking as an example the Yarlung Zangbo River Basin, one of the most important river basins in the Qinghai-Tibet Plateau. NDVI products were acquired from two public sources: Global Inventory Modeling and Mapping Studies (GIMMS) Advanced Very High Resolution Radiometer (AVHRR) and Moderate-Resolution Imaging spectroradiometer (MODIS). After having been extended using the new framework, the new time series of NDVI covers a 384 months period (1982–2013), 84 months longer than previous time series of NDVI product, greatly facilitating NDVI related scientific research. In the new framework, the Gauss Filtering Method was employed to filter out noise in the NDVI product. Next, the standard method was introduced to enhance the comparability of the two data sources, and a pixel-based regression method was used to construct NDVI-extending models with one pixel after another. The extended series of NDVI fit well with original AVHRR-NDVI. With the extended time-series, temporal trends and spatial heterogeneity of NDVI in the study area were studied. Principal influencing factors on NDVI were further determined. The monthly NDVI is highly correlated with air temperature and precipitation in terms of climatic change wherein the spatially averaged NDVI slightly increases in the summer and has increased in temperature and decreased in precipitation in the 32 years period. The spatial heterogeneity of NDVI is in accordance with the seasonal variation of the two climate-change factors. All of these findings can provide valuable scientific support for water-land resources exploration in the third-polar region of the world.

HJ-1A/B is the first small satellite constellation built by China for environmental and disaster monitoring and forecasting. The satellite group has a 2-day repetition cycle and 30 m spatial resolution (charge coupled device camera). Thus, HJ-1A/B can provide hyper-temporal normalized difference vegetation index (NDVI) time series with a medium–high spatial resolution. However, the quality of the HJ NDVI time series can be abnormally low due to a number of factors, such as cloud cover, continuous fog, and haze. In the rainy season or in areas with serious atmospheric pollution, low-quality series often appear in succession, which is referred to as an abnormal segment. Neither the composition method nor quality flags satisfactorily solve this problem; therefore, a large amount of noise and long periods of abnormally low values often remain in HJ NDVI time series. This article presents a method to reconstruct the abnormal segments in HJ NDVI time series with the assistance of Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI time series. The cointegration test was adopted to decide whether MODIS can be used for the reconstruction of NDVI time series for the corresponding HJ image pixels. Statistical quality control methods were used for singling out the abnormal segments in the HJ NDVI time series and establishing an error correction model that combines MODIS and HJ NDVI time series to perform the reconstruction. The study area is located in Jiangsu Province, China. Four-year (2009–2012) HJ multispectral images that cover the study area were used. The results show that abnormal segments in the HJ NDVI time series can be corrected using the proposed method. In a particular year, this method can decrease the root mean square error between the HJ NDVI time series and the reference sequence by 52.5%.

ABSTRACTChange detection within non-stationary and unequally spaced remote sensing time series has become a key methodology for a broad range of environmental applications. A new method of analysing vegetation variation over lands is proposed. Four regions in northern Tunisia with various characteristics are selected, and a non-stationary and unequally spaced Normalized Difference Vegetation Index (NDVI) time series is obtained for each region since 2000. The Landsat 7 remote sensing satellite imagery with insignificant cloud-shadow coverage is used to calculate the NDVI after atmospheric correction. The Least-Squares Wavelet (LSWAVE) software is implemented to rigorously analyse each NDVI time series and study the relationship between the vegetation of olive trees and temperature/precipitation in one of the regions. To investigate possible effects of temperature on the green cover caused by increasing water salinity, the coherency between the NDVI and sea surface temperature time series is also shown in the region of Lake Ichkeul in Tunisia.

An L1-regularized variational approach for NDVI time-series reconstruction considering inter-annual seasonal similarity

In this study, we present an operational methodology for mapping irrigated areas at plot scale, which overcomes the limitation of terrain data availability, using Sentinel-1 (S1) C-band SAR (synthetic-aperture radar) and Sentinel-2 (S2) optical time series. The method was performed over a study site located near Orléans city of north-central France for four years (2017 until 2020). First, training data of irrigated and non-irrigated plots were selected using predefined selection criteria to obtain sufficient samples of irrigated and non-irrigated plots each year. The training data selection criteria is based on two irrigation metrics; the first one is a SAR-based metric derived from the S1 time series and the second is an optical-based metric derived from the NDVI (normalized difference vegetation index) time series of the S2 data. Using the newly developed irrigation event detection model (IEDM) applied for all S1 time series in VV (Vertical-Vertical) and VH (Vertical-Horizontal) polarizations, an irrigation weight metric was calculated for each plot. Using the NDVI time series, the maximum NDVI value achieved in the crop cycle was considered as a second selection metric. By fixing threshold values for both metrics, a dataset of irrigated and non-irrigated samples was constructed each year. Later, a random forest classifier (RF) was built for each year in order to map the summer agricultural plots into irrigated/non-irrigated. The irrigation classification model uses the S1 and NDVI time series calculated over the selected training plots. Finally, the proposed irrigation classifier was validated using real in situ data collected each year. The results show that, using the proposed classification procedure, the overall accuracy for the irrigation classification reaches 84.3%, 93.0%, 81.8%, and 72.8% for the years 2020, 2019, 2018, and 2017, respectively. The comparison between our proposed classification approach and the RF classifier built directly from in situ data showed that our approach reaches an accuracy nearly similar to that obtained using in situ RF classifiers with a difference in overall accuracy not exceeding 6.2%. The analysis of the obtained classification accuracies of the proposed method with precipitation data revealed that years with higher rainfall amounts during the summer crop-growing season (irrigation period) had lower overall accuracy (72.8% for 2017) whereas years encountering a drier summer had very good accuracy (93.0% for 2019).

A practical approach to reconstruct high-quality Landsat NDVI time-series data by gap filling and the Savitzky–Golay filter

Validation of synthetic daily Landsat NDVI time series data generated by the improved spatial and temporal data fusion approach

Normalized Difference Vegetation Index (NDVI) is one of the most widely used indicators for monitoring the vegetation coverage in land surface. The time series features of NDVI are capable of reflecting dynamic changes of various ecosystems. Calculating NDVI via Moderate Resolution Imaging Spectrometer (MODIS) and other wide-swath remotely sensed images provides an important way to monitor the spatial and temporal characteristics of large-scale NDVI. However, difficulties are still existed for ecologists to extract such information correctly and efficiently because of the problems in several professional processes on the original remote sensing images including radiometric calibration, geometric correction, multiple data composition and curve smoothing. In this study, we developed an efficient and convenient online toolbox for non-remote sensing professionals who want to extract NDVI time series with a friendly graphic user interface. It is based on Java Web and Web GIS technically. Moreover, Struts, Spring and Hibernate frameworks (SSH) are integrated in the system for the purpose of easy maintenance and expansion. Latitude, longitude and time period are the key inputs that users need to provide, and the NDVI time series are calculated automatically.

Normalized difference vegetation index (NDVI) time series data, derived from optical images, play a crucial role for crop mapping and growth monitoring. Nevertheless, optical images frequently exhibit spatial and temporal discontinuities due to cloudy and rainy weather conditions. Existing algorithms for reconstructing NDVI time series using multi-source remote sensing data still face several challenges. In this study, we proposed a novel method, an enhanced gap-filling and Whittaker smoothing (EGF-WS), to reconstruct NDVI time series (EGF-NDVI) using Google Earth Engine. In EGF-WS, NDVI calculated from MODIS, Landsat-8, and Sentinel-2 satellites were combined to generate high-resolution and continuous NDVI time series data. The MODIS NDVI was employed as reference data to fill missing pixels in the Sentinel–Landsat NDVI (SL-NDVI) using the gap-filling method. Subsequently, the filled NDVI was smoothed using a Whittaker smoothing filter to reduce residual noise in the SL-NDVI time series. With reference to the all-round performance assessment (APA) metrics, the performance of EGF-WS was compared with the conventional gap-filling and Savitzky–Golay filter approach (GF-SG) in Fusui County of Guangxi Zhuang Autonomous Region. The experimental results have demonstrated that the EGF-WS can capture more accurate spatial details compared with GF-SG. Moreover, EGF-NDVI of Fusui County exhibited a low root mean square error (RMSE) and a high coefficient of determination (R2). In conclusion, EGF-WS holds significant promise in providing NDVI time series images with a spatial resolution of 10 m and a temporal resolution of 8 days, thereby benefiting crop mapping, land use change monitoring, and various ecosystems, among other applications.

High quality Normalized Difference Vegetation Index (NDVI) time series data are important for regional and global ecological applications. However, missing observations in cloudy and shaded areas in current high-resolution satellite data severely limit their use. Traditional temporal filtering reconstruction methods are easily affected by frequent fluctuations, large data gaps and irregular noise in the observations. Meanwhile, satellite images often lack complete and accurate real data as training sets, which limits deep learning-based reconstruction methods. To address these problems, this paper proposes a reconstruction method with Landsat-8 NDVI time-series images based on bidirectional long-short memory networks (Bi-LSTM). The core of the method is a sequence-to-sequence time series reconstruction model using Bi-LSTM as an encoder, decoder, and additionally adding a mask layer to locate irregular gaps in the time series data. To train the model, a multi-scale continuous NDVI time series dataset was constructed using near-surface observations and satellite fused images. Simulation experiments were conducted in 48 areas covering PhenoCam sites in North America. The coefficient of determination (R2) between the simulation results of this method and the real observations reached 0.7024 with an absolute bias of -0.0080, which outperformed the filtering method both in predicting missing cloud-covered areas and in extracting key nodes for vegetation growth. This method is pre-trained on a large-scale ground sample dataset before fine-tuning the parameters for the scenarios, which can be easily and quickly applied to generate long-term continuous NDVI datasets at a large spatial scale. In addition, the analysis of the phenology monitoring in the experimental areas shows that the results extracted from the reconstructed results of this method is very close to the PhenoCam-measured values, which is significantly improved compared to the extraction results using original Landsat data only, and at the same time shows more spatial detail compared to the MODIS climate products. This indicates that the 30m gap-free NDVI time series is sufficient for analysis and understanding of climate dynamics in heterogeneous areas with multiple vegetation growth patterns, mixing plant components and microclimates variation, which are difficult to be identified by the coarse spatial resolution phenology product.

Informational analysis of MODIS NDVI and EVI time series of sites affected and unaffected by wildfires

Estimating root zone soil moisture at distant sites using MODIS NDVI and EVI in a semi-arid region of southwestern USA

Timely and accurate crop type distribution maps are an important inputs for crop yield estimation and production forecasting as multi-temporal images can observe phenological differences among crops. Therefore, time series remote sensing data are essential for crop type mapping, and image composition has commonly been used to improve the quality of the image time series. However, the optimal composition period is unclear as long composition periods (such as compositions lasting half a year) are less informative and short composition periods lead to information redundancy and missing pixels. In this study, we initially acquired daily 30 m Normalized Difference Vegetation Index (NDVI) time series by fusing MODIS, Landsat, Gaofen and Huanjing (HJ) NDVI, and then composited the NDVI time series using four strategies (daily, 8-day, 16-day, and 32-day). We used Random Forest to identify crop types and evaluated the classification performances of the NDVI time series generated from four composition strategies in two studies regions from Xinjiang, China. Results indicated that crop classification performance improved as crop separabilities and classification accuracies increased, and classification uncertainties dropped in the green-up stage of the crops. When using daily NDVI time series, overall accuracies saturated at 113-day and 116-day in Bole and Luntai, and the saturated overall accuracies (OAs) were 86.13% and 91.89%, respectively. Cotton could be identified 40∼60 days and 35∼45 days earlier than the harvest in Bole and Luntai when using daily, 8-day and 16-day composition NDVI time series since both producer’s accuracies (PAs) and user’s accuracies (UAs) were higher than 85%. Among the four compositions, the daily NDVI time series generated the highest classification accuracies. Although the 8-day, 16-day and 32-day compositions had similar saturated overall accuracies (around 85% in Bole and 83% in Luntai), the 8-day and 16-day compositions achieved these accuracies around 155-day in Bole and 133-day in Luntai, which were earlier than the 32-day composition (170-day in both Bole and Luntai). Therefore, when the daily NDVI time series cannot be acquired, the 16-day composition is recommended in this study.

Foliar dynamics in tropical southern Africa are examined using meteorological satellite observations (NOAA-AVHRR) collected from 1981–1990, processed as monthly Normalized Difference Vegetation Index (NDVI) images, and resampled to 7.6 km resolution. Time series of NDVI and raingauge data are presented and analyzed using a variety of statistics. The analysis of time series from individual locations revealed positive correlations between NDVI and rainfall at semiarid locations where rainfall tended to be highly variable; whereas the relationships between these variables was insignificant in more mesic sites where the climate tended to be more predictable. In addition, there appeared to be an annual rainfall threshold of approximately 600 mm beyond which relationships between rainfall and NDVI were insignificant at the monthly time scale. Relationships between rainfall and NDVI were stronger at annual time scale, which suggests that factors other than contemporaneous rainfall account for photosynthetic activity in any given growing season. Using a rainfall surface and NDVI imagery, a large area of ‘early’ greening behavior is identified, which corresponded approximately to the distribution of mesic, plateau woodlands. These so-called, ‘miombo’ woodlands may be especially vulnerable if the arrival of spring rainfall were to undergo a positive shift in phase.

Sentinel-1, a pair of Synthetic Aperture Radar (SAR) sensors, provides valuable all-weather and day-night imaging capabilities, enabling continuous monitoring of vegetation dynamics even in the presence of cloud cover. SAR sensors excel at penetrating vegetation canopies and providing information on crucial factors like vegetation structure, biomass, and moisture content. However, most remote sensing vegetation studies have primarily relied on optical data, benefiting from longer historical datasets but facing challenges due to atmospheric interference, limited temporal resolution. Moreover, there is no research assessing the sensitivity of optical and radar data to the dynamics of vegetation in Mozambique. This study investigates the sensitivity of the Sentinel-1 (S-1) backscatter signal to vegetation dynamics over Mozambique. We compare it with the Normalized Difference Vegetation Index (NDVI) from MODIS data and explore its relationship with precipitation variability and droughts across different land covers, including forest, cropland, herbaceous vegetation, and herbaceous wetland in Mozambique.The Sentinel-1 VV and VH polarized images were used to calculate the Cross Ratio (CR=VH/VV). Temporal behaviors of CR S-1 and MODIS NDVI were analyzed from 2017 to 2022, examining seasonal patterns, inter-annual variability, trends, and outliers. NDVI anomalies were calculated to identify the spatial and temporal occurrence of agricultural droughts, while CHIRPS precipitation data was utilized to detect fluctuations in the CR S-1 and NDVI time series relative to precipitation. The analysis revealed distinct seasonality in the CR time series data across all land cover types. Notably, croplands, herbaceous vegetation, and herbaceous wetlands exhibited a consistent increase in CR during winter months, followed by a decline during summer months. In contrast, forests displayed an inverse trend, with CR decreasing in winter and increasing in summer. Furthermore, no pronounced CR patterns were observed in herbaceous wetlands during 2019, coinciding with the agricultural drought between 2018 and 2019. Additionally, the seasonality of MODIS NDVI time series remained consistent across all land cover types, with no noticeable differences. It was observed that fluctuations in NDVI time series preceded those in CR for these specific land cover types, suggesting a potential correlation with photosynthetic activity and subsequent biomass production. Significantly, this trend was found to be opposite in forested areas. Overall, the CR trend exhibited a clear correlation with rainfall seasonality across the various land cover types, except for forests where an inverse relationship was observed. On the other hand, NDVI demonstrated a higher sensitivity to changes in precipitation across the different land cover categories.These findings highlight the unique sensitivity of Sentinel-1 SAR data in capturing the intricate dynamics of vegetation across diverse land cover types in Mozambique, providing valuable complementary information to traditional optical data sources.